2.在约会网站上使用k近邻算法
在约会网站上使用k近邻算法
思路步骤：
1. 收集数据：提供文本文件。
2. 准备数据：使用Python解析文本文件。
3. 分析数据：使用Matplotlib画二维扩散图。
4. 训练算法：此步骤不适用于k近邻算法。
5. 测试算法：使用海伦提供的部分数据作为测试样本。
测试样本和非测试样本的区别在于：测试样本是已经完成分类的数据，如果预测分类与实际类别不同，则标记为一个错误。
6. 使用算法：产生简单的命令行程序，然后海伦可以输入一些特征数据以判断对方是否为自己喜欢的类型。
正式开始：
第1步.收集数据，提供文本文件datingTestSet.txt：
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- 14488 7.153469 1.673904 smallDoses
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- 38344 1.669788 0.134296 didntLike
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- 35483 12.273169 1.508053 largeDoses
- 50242 3.723498 0.831917 didntLike
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- 5569 4.875435 0.728658 smallDoses
- 51052 4.680098 0.625224 didntLike
- 77372 15.299570 0.331351 didntLike
- 43673 1.889461 0.191283 didntLike
- 61364 7.516754 1.269164 didntLike
- 69673 14.239195 0.261333 didntLike
- 15669 0.000000 1.250185 smallDoses
- 28488 10.528555 1.304844 largeDoses
- 6487 3.540265 0.822483 smallDoses
- 37708 2.991551 0.833920 didntLike
- 22620 5.297865 0.638306 smallDoses
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- 41611 0.230453 1.151996 didntLike
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- 43605 0.120460 1.352013 didntLike
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- 44952 3.424649 1.004504 didntLike
- 17934 0.000000 0.147573 smallDoses
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- 27268 4.398172 0.975024 didntLike
- 5477 4.276823 1.174874 smallDoses
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- 41539 10.393591 1.663724 largeDoses
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- 9171 4.711960 0.194167 smallDoses
- 28122 8.768099 1.108041 largeDoses
- 34095 11.502519 0.545097 largeDoses
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- 11210 3.929456 0.025466 smallDoses
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- 16871 6.799831 1.221171 smallDoses
- 39776 8.752758 0.484418 largeDoses
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- 26501 11.573696 1.061347 largeDoses
- 37433 8.038764 1.083910 largeDoses
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- 27742 9.005850 0.548737 largeDoses
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- 42414 14.075597 0.695934 largeDoses
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- 26547 6.024471 0.486215 largeDoses
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- 10933 0.000000 0.107475 smallDoses
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- 51213 7.842646 1.060636 largeDoses
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- 4805 11.438702 1.556334 largeDoses
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- 17591 7.875477 0.227085 smallDoses
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- 42814 0.000000 1.515293 didntLike
- 14738 3.396030 0.633977 smallDoses
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- 58736 4.589016 1.672600 didntLike
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- 27698 10.905159 0.619091 largeDoses
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- 56117 10.647170 0.980141 largeDoses
- 39514 0.000000 0.481918 didntLike
- 26627 8.503025 0.830861 largeDoses
- 16525 0.436880 1.395314 smallDoses
- 24368 6.127867 1.102179 didntLike
- 22160 12.112492 0.359680 largeDoses
- 6030 1.264968 1.141582 smallDoses
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- 19600 11.155826 0.335131 largeDoses
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- 37068 2.063741 1.125946 didntLike
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- 42878 11.464879 1.481589 largeDoses
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- 10591 10.226164 0.804403 largeDoses
- 70096 10.965926 1.212328 didntLike
- 53151 2.129921 1.477378 didntLike
- 11992 0.000000 1.606849 smallDoses
- 33114 9.489005 0.827814 largeDoses
- 7413 0.000000 1.020797 smallDoses
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- 58668 6.556676 0.055183 didntLike
- 35018 9.959588 0.060020 largeDoses
- 70843 7.436056 1.479856 didntLike
- 14011 0.404888 0.459517 smallDoses
- 35015 9.952942 1.650279 largeDoses
- 70839 15.600252 0.021935 didntLike
- 3024 2.723846 0.387455 smallDoses
- 5526 0.513866 1.323448 smallDoses
- 5113 0.000000 0.861859 smallDoses
- 20851 7.280602 1.438470 smallDoses
- 40999 9.161978 1.110180 largeDoses
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- 35432 7.398380 0.684218 largeDoses
- 53711 12.149747 1.389088 largeDoses
- 64371 9.149678 0.874905 didntLike
- 9289 9.666576 1.370330 smallDoses
- 60613 3.620110 0.287767 didntLike
- 18338 5.238800 1.253646 smallDoses
- 22845 14.715782 1.503758 largeDoses
- 74676 14.445740 1.211160 didntLike
- 34143 13.609528 0.364240 largeDoses
- 14153 3.141585 0.424280 smallDoses
- 9327 0.000000 0.120947 smallDoses
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- 9193 0.510310 0.016395 smallDoses
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- 2371 4.289375 0.012563 smallDoses
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- 2299 3.733617 0.698269 smallDoses
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- 4659 2.502627 0.184223 smallDoses
- 17582 6.382129 0.876581 smallDoses
- 27750 8.546741 0.128706 largeDoses
- 9868 2.694977 0.432818 smallDoses
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- 18190 2.068962 0.429927 smallDoses
- 11145 3.410627 0.631838 smallDoses
- 68846 9.974715 0.669787 didntLike
- 26575 10.650102 0.866627 largeDoses
- 48111 9.134528 0.728045 largeDoses
- 43757 7.882601 1.332446 largeDoses
1.2把文本文件初步处理，分类换成数字datingTestSet2.txt：
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48111 9.134528 0.728045 3
43757 7.882601 1.332446 3
第2步.准备数据：使用Python解析文本文件,文件名kNN.py
from numpy import * #导入科学计算包
import operator #运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数 #文件转矩阵函数开始
def file2matrix(filename):
fr=open(filename) #读取文档
arraylines=fr.readlines() #读取每行，结果为列表格式
lengthlines=len(arraylines) #获取列表长度，相当于文档行数
mats=zeros((lengthlines,3)) #创建一个以0填充的矩阵：（lengthlines行，3列）
classLabelVector = [] #创建分类标签列表
i=0 for line in arraylines: #处理列表
line=line.strip() #去除每行列表两边空格、回车等
listFromLine=line.split('\t') #用Tab键 分割列表为：[40920\t8.326976\t0.953952\tlargeDoses]（\t千万别写错/t，否则报错：“could not convert string to float:”）
mats[i,:]=listFromLine[0:3] #把列表前3个数字填入mats的矩阵：[40920\t8.326976\t0.953952]
classLabelVector.append(int(listFromLine[-1])) #把列表最后一项添加入classLabelVector列表，顺序为先进排在前面，后进排后面（如果用文件datingTestSet.txt，则原int(listFromLine[-1])去除int，否则报错）
i+=1
return mats,classLabelVector #返回训练矩阵，和对应的分类标签
运行：可以再最下面加入：
if __name__=='__main__':
#groups,labels=createDataSet()
#print(classify0([1,0], groups, labels, 2)) a,b=file2matrix('datingTestSet2.txt')
print(a)
print(b)
运行2：或在命令窗输入：import kNN
>>> import kNN
>>> reload(kNN) #kNN有变化时，重加载
>>> datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
第3步. 分析数据：使用Matplotlib画二维扩散图
3.1 在KNN.py，和datingTestSet.txt 文件夹内，运行命令窗口：
>>> import kNN
>>> datingDataMat, datingLabels = kNN.file2matrix('datingTestSet.txt')
>>> import matplotlib
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.scatter(datingDataMat[:,1], datingDataMat[:,2])
>>> plt.show()
得到下图：（x：玩视频游戏所耗时间百分比，y：每周所消费的冰淇淋公升数）
3.2 由于没有使用样本分类的特征值，我们很难从图2-3中看到任何有用的数据模式信息。一般来说，我们会采用色彩或其他的记号来标记不同样本分类，以便更好地理解数据信息。
在文件夹下新建一个名为test.py的文件，输入如下代码：
import knn,matplotlib
import matplotlib.pyplot as plt
from numpy import array datingDataMat, datingLabels = knn.file2matrix('datingTestSet.txt') fig = plt.figure()
ax = fig.add_subplot(111)
#ax.scatter(datingDataMat[:,1], datingDataMat[:,2])#没有分类信息的绘图 #以下for循环用于把分类字符串转换成数字：largeDoses=3,smallDoses=2,didntLike=1,（如果用文件datingTestSet2.txt则不用此循环）
labels=[]
for label in datingLabels:
if label=='largeDoses':
labels.append(3)
elif label=='smallDoses':
labels.append(2)
else:
labels.append(1) #以下for循环用于绘图，包含分类信息
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(labels), 15.0*array(labels))
plt.show()
上代码将绘制如下图：
代码解释：上述代码利用变量datingLabels存储的类标签属性，在散点图上绘制了色彩不等、尺寸不同的点。
4.准备数据：归一化数值
表2-3】约会网站原始数据改进之后的样本数据：
【公式：计算两样本间距离公式】：
多个样本同样适用：
例】对于本例，计算样本3、4间距离是：
为什么要数值规一】：上面方程中数字差值最大的属性对计算结果的影响最大，也就是说，每年获取的飞行常客里程数对于计算结果的影响将远远大于表2-3中其他两个特征——玩视频游戏的和每周消费冰淇淋公升数——的影响。而产生这种现象的唯一原因，仅仅是因为飞行常客里程数远大于其他特征值。但海伦认为这三种特征是同等重要的，因此作为三个等权重的特征之一，飞行常客里程数并不应该如此严重地影响到计算结果。
常用规一方式】我们通常采用的方法是将数值归一化，如将取值范围处理为0到1或者-1到1之间。
【规一公式】：newValue = (oldValue-min)/(max-min)
规一公式解释：
min：数据集中的 每一列 的 最小特征值，
max：数据集中的 每一列 的 最大特征值
newValue：要求的最终规一值
oldvalue：要换成规一值的值
最终函数如下，也写在knn.py里：
from numpy import * #导入科学计算包
import operator #运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数 #规一化大数值函数：用于处理部分数值太大情形
def autoNorm(dataSet):
minVals = dataSet.min(0) #返回每一列的最小值组成的列表，结果[0. 0. 0.00156]
maxVals = dataSet.max(0) #返回每一列的最大值组成的列表，结果[9.1273000e+04 2.0919349e+01 1.6955170e+00](e+04表示：9.1273*1000)
ranges = maxVals - minVals #返回最大-最小差值，结果[9.1273000e+04 2.0919349e+01 1.6943610e+00]
normDataSet = zeros(shape(dataSet)) #=zero(shape(1000,3))=1000行3列以0填充的矩阵
m = dataSet.shape[0] #获取维度的第一个数据即行数，m=1000
normDataSet = dataSet - tile(minVals, (m, 1)) #原数矩阵-最小数集矩阵（把最小列表转换成1000行1列与输入集一样的矩阵后才能运算）
normDataSet = normDataSet / tile(ranges, (m, 1)) # ❶ 特征值相除 （完成规一运算(oldValue-min)/(max-min)）
return normDataSet, ranges, minVals #返回：规一值，(max-min)，最小值 #运行函数部分
if __name__=='__main__':
datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
normMat, ranges, minVals = autoNorm(datingDataMat)
代码解释：也可以只返回normMat矩阵，但是下一节我们将需要取值范围和最小值归一化测试数据
运行2：或在命令窗口进入knn.py所在目录后运行（记得注释掉if __name__..：以下的内容）：
>>> reload(kNN)
>>> normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
>>> normMat
array([[ 0.33060119, 0.58918886, 0.69043973],
[ 0.49199139, 0.50262471, 0.13468257],
[ 0.34858782, 0.68886842, 0.59540619],
...,
[ 0.93077422, 0.52696233, 0.58885466],
[ 0.76626481, 0.44109859, 0.88192528],
[ 0.0975718 , 0.02096883, 0.02443895]])
>>> ranges
array([ 8.78430000e+04, 2.02823930e+01, 1.69197100e+00])
>>> minVals
array([ 0. , 0. , 0.001818])
5.测试算法：作为完整程序验证分类器
程序如下：
#以下函数：找一些数据进行算法测试，算出错误率
def datingClassTest():
hoRatio = 0.1 #取出 10%
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #调用之前写的函数将数据转矩阵
normMat, ranges, minVals = autoNorm(datingDataMat) #将数值规一化
m = normMat.shape[0] #求出数值行数，1000
numTestVecs = int(m*hoRatio) #取出百分之十用于测试，1000*0.1=100
errorCount = 0.0 #用于计算错误总数
for i in range(numTestVecs): #循环100个
classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3) #k值函数（测试集0-99行所有列，学习集100-最后一行所有列，标签从100行开始，k=3
print ("the classifier came back with: %s, the real answer is: %s" % (classifierResult, datingLabels[i])) #括号内（函数算出的结果，标签实际结果）
if (classifierResult != datingLabels[i]): errorCount += 1.0 #如果学习结果!= 实际结果，错误+1
print ("the total error rate is: %f" % (errorCount/float(numTestVecs))) #错误比率=错误数/总测试数
print (errorCount) #输出错误数
运行方法1：在最后加入如下代码：
if __name__=='__main__':
datingClassTest()
运行方法2：在文件夹内打开命令窗口：
import knn
>>> knn.datingClassTest()
6.使用算法：构建完整可用系统
最后总结综合一下函数：
from numpy import * #导入科学计算包
import operator #运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数 def createDataSet():
group=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels=['A','A','B','B']
return group,labels #k近邻分类函数
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]#求数据集的维度
diffMat = tile(inX, (dataSetSize,1)) - dataSet
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1) #axis=0是按照行求和，axis=1是按照列进行求和
distances = sqDistances**0.5 #开根号
sortedDistIndicies = distances.argsort()#把向量中每个元素进行排序，结果是元素的索引形成的向量
classCount={}
#❷ （以下两行）选择距离最小的k个点
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #❸ 排序。 3.5以上版本，原classCount.iteritems()变为classCount.items()
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0] #文件转矩阵函数开始
def file2matrix(filename):
fr=open(filename) #读取文档
arraylines=fr.readlines() #读取每行，结果为列表格式
lengthlines=len(arraylines) #获取列表长度，相当于文档行数
mats=zeros((lengthlines,3)) #创建一个以0填充的矩阵：（lengthlines行，3列） classLabelVector = [] #创建分类标签列表
i=0
for line in arraylines: #处理列表
line=line.strip() #去除每行列表两边空格、回车等
listFromLine=line.split('\t') #用Tab键 分割列表为：[40920\t8.326976\t0.953952\tlargeDoses]（\t千万别写错/t，否则报错：“could not convert string to float:”）
mats[i,:]=listFromLine[0:3] #把列表前3个数字填入mats的矩阵：[40920\t8.326976\t0.953952]
#if listFromLine[-1]=='largeDoses':
classLabelVector.append(int(listFromLine[-1])) #把列表最后一项添加入classLabelVector列表，顺序为先进排在前面，后进排后面（原int()去除，否则报错）
i+=1
# # 以下for循环用于把分类字符串转换成数字：largeDoses=3,smallDoses=2,didntLike=1（用于兼容py3.x以后版本字符不能直接int为数字问题）
# labels = []
# for label in classLabelVector:
# if label == 'largeDoses':
# labels.append(3)
# elif label == 'smallDoses':
# labels.append(2)
# else:
# labels.append(1)
return mats,classLabelVector #返回训练矩阵，和对应的分类标签 #规一化大数值函数：用于处理部分数值太大情形
def autoNorm(dataSet):
minVals = dataSet.min(0) #返回每一列的最小值组成的列表，结果[0. 0. 0.00156]
maxVals = dataSet.max(0) #返回每一列的最大值组成的列表，结果[9.1273000e+04 2.0919349e+01 1.6955170e+00](e+04表示：9.1273*1000)
ranges = maxVals - minVals #返回最大-最小差值，结果[9.1273000e+04 2.0919349e+01 1.6943610e+00]
normDataSet = zeros(shape(dataSet)) #=zero(shape(1000,3))=1000行3列以0填充的矩阵
m = dataSet.shape[0] #获取维度的第一个数据即行数，m=1000
normDataSet = dataSet - tile(minVals, (m, 1)) #原数矩阵-最小数集矩阵（把最小列表转换成1000行1列与输入集一样的矩阵后才能运算）
normDataSet = normDataSet / tile(ranges, (m, 1)) # ❶ 特征值相除 （完成规一运算(oldValue-min)/(max-min)）
return normDataSet, ranges, minVals #返回：规一值，(max-min)，最小值 #以下函数：找一些数据进行算法测试，算出错误率
def datingClassTest():
hoRatio = 0.1 #取出 10%
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #调用之前写的函数将数据转矩阵
normMat, ranges, minVals = autoNorm(datingDataMat) #将数值规一化
m = normMat.shape[0] #求出数值行数，1000
numTestVecs = int(m*hoRatio) #取出百分之十用于测试，1000*0.1=100
errorCount = 0.0 #用于计算错误总数
for i in range(numTestVecs): #循环100个
classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3) #k值函数（测试集0-99行所有列，学习集100-最后一行所有列，标签从100行开始，k=3
print ("the classifier came back with: %s, the real answer is: %s" % (classifierResult, datingLabels[i])) #括号内（函数算出的结果，标签实际结果）
if (classifierResult != datingLabels[i]): errorCount += 1.0 #如果学习结果!= 实际结果，错误+1
print ("the total error rate is: %f" % (errorCount/float(numTestVecs))) #错误比率=错误数/总测试数
print (errorCount) #输出错误数 #面向个人的函数
def classifyPerson():
resultList = ['一点也不喜欢','有一点点啦', '非常喜欢']
percentTats = float(input("玩电子游戏的时间百分比?"))
ffMiles = float(input("每年获得的飞行常客里程?"))
iceCream = float(input("每年消耗的冰淇淋升数?"))
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
normMat, ranges, minVals = autoNorm(datingDataMat)
inArr = array([ffMiles, percentTats, iceCream])
classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels,3)
print ("你可能喜欢这个人: ",resultList[classifierResult - 1]) if __name__=='__main__':
classifyPerson()
# matss,labelss=file2matrix('datingTestSet.txt')
# print(matss,labelss)
#datingClassTest() # datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
# normMat, ranges, minVals = autoNorm(datingDataMat) # #groups,labels=createDataSet()
# #print(classify0([1,0], groups, labels, 2))
#
# a,b=file2matrix('datingTestSet.txt')
# print(a)
# print(b)
运行：
runfile('C:/Users/Administrator/Desktop/机学-pdf/机器学习实战/knn.py', wdir='C:/Users/Administrator/Desktop/机学-pdf/机器学习实战')
玩电子游戏的时间百分比?>? 50
每年获得的飞行常客里程?>? 90000
每年消耗的冰淇淋升数?>? 800
你可能喜欢这个人: 非常喜欢
目录内容：
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